Planning.
Model predictive path integral (MPPI) [1]} is used for planning the subgoals as in [2]}.
We first sample 1,024 sequences of latent codes from the prior distribution \(p(u)\)  and chooses the optimal plan through the planning procedure described in Sec. REF .
Then we refine the plan through 5 MPPI iterations.
In each iteration, we add Gaussian noises scaled by 1.0, 0.5, 0.2, 0.1, 0.1 to the selected sequence of latent codes from the previous iteration.
In Eqn. REF , we set the weight \(\eta _1 = 1\) , \(\eta _2 = 1\)  and \(\eta _3 = 0.1\) . The planner produces 4 subgoals and the last subgoal is replaced with the final goal.
The planner switches to the next subgoal in two conditions: (1) when the current subgoal is reached (2) if the current subgoal is not reached within a time budget of \(h\)  steps.
We set \(h=\Delta t\)  to spare a more generous time budget for the trained policy to reach each subgoal, where \(\Delta t\)  is the time interval for training the affordance model described in Sec. REF .
